Non-Pathological Psychological Distress among Mainland Chinese in Canada and Its Sociodemographic Risk Factors amidst the Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated health inequalities, with a potentially heightened mental health risk for Mainland Chinese in Canada, given the rising anti-Chinese discrimination, and barriers in assessing health services. In this context, this study aimed to assess non-pathological psychological distress towards COVID-19 and identify its sociodemographic risk factors among Mainland Chinese in Canada at the early stages of the pandemic. Methods: A sample of 731 Mainland Chinese aged 16 or older completed an on-line survey to examine their attitudes, behavioural, and psychological responses towards COVID-19. Non-pathological psychological distress was assessed with a 7-item self-report scale to capture common emotional reactions towards COVID-19. Results: A factor analysis revealed a single-factor structure of the 7-item COVID-19 psychological distress scale (Eigen λ = 3.79). A composite psychological distress index (PDI) score was calculated from these items and used as the outcome variable. Multivariate regression models identified age, financial satisfaction, health status, and perceived/experienced discrimination as significant predictors of psychological distress (ps ≤ 0.05). Conclusions: Mainland Chinese in Canada who were over 25, in poor financial/health status, or with perceived/experienced discrimination were at a higher risk for COVID-19-related psychological distress. The health inequity across these factors would inform the services to mitigate mental health risk in minority groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".